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Reference-lines steered guide assignment and update for pareto-based many-objective particle swarm optimization

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Abstract

The reference-lines-framework has been successfully used for developing efficient many-objective evolutionary algorithms. In this paper, the concepts and methodologies of such evolutionary algorithms are adapted in the parlance of multi-objective particle swarm optimization (MOPSO) for addressing the challenges of assigning and updating the global and local guides. The proposed algorithm, which is referred to as RMaOPSO, is developed via five modules using the framework so that a diverse set of guides can be selected to steer the search of MOPSO toward the Pareto-optimal front. The modules include global guide assignment, local and global guide update, line assignment to the guides and swarm, and evolutionary search for global guides. The proposed algorithm is tested on DTLZ and WFG test instances of 3-, 5-, 8-, 10- and 15- objectives. Results obtained from RMaOPSO show its efficacy over six multi-objective evolutionary and MOPSO algorithms from the literature.

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Notes

  1. NSGA-III code developed by [49] is used, which is available in the public domain.

  2. The codes of SPEA/R, VaEA and MaPSO are provided by the authors.

  3. The source codes of dMOPSO and SMPSO are obtained from the jmetal framework [22].

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  1. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Deepak Sharma, Devang Agarwal & Santosh Kumar

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  1. Deepak Sharma
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Sharma, D., Agarwal, D. & Kumar, S. Reference-lines steered guide assignment and update for pareto-based many-objective particle swarm optimization. Evol. Intel. 16, 89–114 (2023). https://doi.org/10.1007/s12065-021-00644-4

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